Genetic mapping of metabolic biomarkers of cardiometabolic diseases

Cardiometabolic disorders (CMDs) are a major public health problem worldwide. The main goal of this thesis is to characterize the genetic architecture of CMD-related metabolites in a Lebanese cohort. In order to maximise the extraction of meaningful biological information from this dataset, an important part of this thesis focuses on the evaluation and subsequent improvement of the standard methods currently used for molecular epidemiology studies. First, I describe MetaboSignal, a novel network-based approach to explore the genetic regulation of the metabolome. Second, I comprehensively compare the recovery of metabolic information in the different 1H NMR strategies routinely used for metabolic profiling of plasma (standard 1D, spin-echo and JRES). Third, I describe a new method for dimensionality reduction of 1H NMR datasets prior to statistical modelling. Finally, I use all this methodological knowledge to search for molecular biomarkers of CMDs in a Lebanese population. Metabolome-wide association analyses identified a number of metabolites associated with CMDs, as well as several associations involving N-glycan units from acute-phase glycoproteins. Genetic mapping of these metabolites validated previously reported gene-metabolite associations, and revealed two novel loci associated with CMD-related metabolites. Collectively, this work contributes to the ongoing efforts to characterize the molecular mechanisms underlying complex human diseases.Open Acces

Multi-level modeling and computational approaches to investigate long-term diabetes complications

Diabetes mellitus is a lifelong, incapacitating disease affecting multiple organs. Worldwide prevalence figures estimate that there are 250 million diabetic patients today and that this number will increase by 50% by 2025. The disease is associated with devastating chronic complications including coronary heart disease, stroke and peripheral vascular disease (macrovascular disease) as well as microvascular disorders, leading to damage of kidneys (nephropathy) and eyes (retinopathy). These complications impose an immense burden on the quality of life of the patients and account for more than 10% of health care costs in Europe. Therefore, novel means to prevent the onset and the progression of these devastating diabetic complications are needed. The aim of the work presented in this thesis is to propose novel computational methods to study diabetes complications with a multi-level approach. Diabetes mellitus is a strongly multifactorial disease, and several risks factors (such as genetic, and environmental factors) are combined together in a complex trait, leading to the onset of the disease. Physiological mechanisms that underlie the disease and the onset and progression of the different complications are still mostly unknown. Given the complex nature of diabetes, the study of the complications can be faced with a multi-level modeling approach. In the general scheme for complex disease, such as diabetes, 3 key elements act together to determine the disease status (outcome) of a patient: i) the phenotype, i.e. the set of all metabolic, anthropometric and clinical variables characterizing the patient, ii) the genotype, i.e. the DNA sequence of the patient, iii) the set of interventions on the patient, i.e. therapies and treatments with drugs. All these 3 variables are connected each other through interactions and have a joint effect on the final outcome of the patient. The multi-level approach allows to disjoint the full problem into sub-problems, focusing only on a set of variables and interaction (reflecting a specific level of information) according to available data. In the present work, 3 main levels of study of diabetes complications are considered, and, for each approach, novel methodologies developed during my PhD are proposed. The 3 levels of study considered in the present work are: i) modeling the effect of genotype on the outcome, ii) modeling the effect of phenotype and treatment on the progression of the outcome, iii) modeling the effect of treatment on the phenotype. In the first level of study, diabetes complications are studied from a static point of view, i.e. without considering their progression over time, and the main objective is to identify the genetic biomarkers that allow to predict the disease state of the patients with the final goal to stratify patients according to the risk of developing the disease. Genome Wide Associations Studies (GWAs) are statistical studies aiming at identify those SNPs able to explain the differences observed for a certain outcome (the disease status) between cases (diseased subjects) and controls (healthy subjects) in a study population. Several methods performing univariate and/or multivariate selection have been used in literature for the identification of genetic markers from GWAs data. In this thesis, a novel algorithm for genetic biomarker selection and subjects classification from genome-wide SNP data has been developed. The algorithm is based on the Naïve Bayes classification framework, enriched by three main features: i) bootstrap aggregating of an ensemble of Naïve Bayes classifiers, ii) a novel strategy for ranking and selecting the attributes used by each classifier in the ensemble, iii) a permutation-based procedure for selecting significant biomarkers, based on their marginal utility in the classification process. The algorithm has been validated on the Wellcome Trust Case-Control Consortium on Type 1 Diabetes and its performance compared with the ones of both a standard Naïve Bayes algorithm and HyperLASSO, a penalized logistic regression algorithm from the state-of-the-art in simultaneous genome-wide data analysis. The second level of study is represented by the dynamic analysis of diabetes complications, where the variable “time” plays a major role. In particular, the objective is to model the onset and the progression of diabetes complications over time, using phenotypic and therapeutic information, with the final goal to estimate a probability for the diabetic patient to develop a certain complication, thus optimizing clinical trials and avoiding invasive and expensive tests. So far, several models of diabetes complications are present in literature, but none is able to flexibly integrate accumulating –omics knowledge (i.e. proteomics, metabolomics, genomics) into a clinical macro-level. The most interesting complication models, in fact, are based on Markov Models (also called state transition model) and use phenotypic information to describe the cohort of interest without the possibility to easily integrate additional information. A new in-silico model for simulating the progression of cardiovascular and kidney complications in diabetic patients is presented. The model proposes, as innovative feature, the use of Dynamic Bayesian Networks (DBNs) for modeling the interactions between variables. Compared to Markov Models, which require as many nodes as the number of combinations of variables’ values, DBNs are more advantageous in handling both the structure and possible additional information, since each variable is simply represented by a node in the network. The model was built relying on data from the Diabetes Control and Complications Trial, a multicenter randomized clinical trial designed to compare intensive with conventional therapy with regard to their effects on the development and progression of the early vascular and neurologic. The developed model is able to predict the progression of the main diabetes complications with an accuracy greater than 95% at a population level. The model is suitable to be used as a decision support tool to help clinicians in the therapy design through cost-effectiveness analysis: exploiting the simulations generated through the model, it is possible, for example, to choose the best strategy between two different therapies for treating a specific cohort of patients. To this aim, a user-interface based on the present model is currently under development. The flexible structure of the model will allow to easily add genotypic information in the next feature as a potential mean to improve predictions. The last level of study focuses on the action of a specific drug on a target phenotype, with the final aim to develop rational means to personalize drug therapy and to ensure maximum efficacy with minimal adverse effects. Focusing on cardiovascular diseases as a direct complication of diabetes, aspirin therapy is an important component of cardiovascular prevention for high risk patients. Aspirin performs its preventive action by inhibiting a key enzyme (the prostaglandin-endoperoxide synthase PTGS-1, also known as cyclooxygenase COX-1) in the cascade leading to the production of thromboxane B2 (TxB2), the major factor involved in the platelets aggregation with consequent formation of thrombi. It is known, from literature, that diabetic patients exhibit a different response to aspirin therapy in comparison to healthy subjects, showing a reduced effectiveness of the drug, which is often referred to as ‘aspirin resistance’. Given the lack of a mathematical characterization of these phenomena, the problem was faced using a pharmacodynamics modeling approach, with an explorative intent. Relaying on biological knowledge retrieved from literature, a partially lumped and partially distributed compartmental model was developed, able to describe: i) the kinetics of COX-1 enzyme, from its production within megakaryocytes in bone-marrow to circulating platelets in blood, ii) the pharmacokinetics and pharmacodynamics of aspirin, i.e. its distribution in the body tissues and its interaction with COX-1. The model was tested using data of serum thromboxane TxB2 recovery levels after aspirin withdrawal in healthy subjects. Possible mechanisms to explain the so-called ‘aspirin resistance’ have been finally discussed

Learning Algorithms for Fat Quantification and Tumor Characterization

Obesity is one of the most prevalent health conditions. About 30% of the world\u27s and over 70% of the United States\u27 adult populations are either overweight or obese, causing an increased risk for cardiovascular diseases, diabetes, and certain types of cancer. Among all cancers, lung cancer is the leading cause of death, whereas pancreatic cancer has the poorest prognosis among all major cancers. Early diagnosis of these cancers can save lives. This dissertation contributes towards the development of computer-aided diagnosis tools in order to aid clinicians in establishing the quantitative relationship between obesity and cancers. With respect to obesity and metabolism, in the first part of the dissertation, we specifically focus on the segmentation and quantification of white and brown adipose tissue. For cancer diagnosis, we perform analysis on two important cases: lung cancer and Intraductal Papillary Mucinous Neoplasm (IPMN), a precursor to pancreatic cancer. This dissertation proposes an automatic body region detection method trained with only a single example. Then a new fat quantification approach is proposed which is based on geometric and appearance characteristics. For the segmentation of brown fat, a PET-guided CT co-segmentation method is presented. With different variants of Convolutional Neural Networks (CNN), supervised learning strategies are proposed for the automatic diagnosis of lung nodules and IPMN. In order to address the unavailability of a large number of labeled examples required for training, unsupervised learning approaches for cancer diagnosis without explicit labeling are proposed. We evaluate our proposed approaches (both supervised and unsupervised) on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans respectively. The proposed segmentation, quantification and diagnosis approaches explore the important adiposity-cancer association and help pave the way towards improved diagnostic decision making in routine clinical practice

The development of a risk index for depression (RID)

&nbsp;This thesis developed a novel methodology for a flexible and modular Risk Index for Depression (RID) that blended data mining and machine learning techniques with traditional statistical techniques. This RID shows great potential for future clinical use.<br /

Modelling individual accessibility using Bayesian networks: A capabilities approach

The ability of an individual to reach and engage with basic services such as healthcare, education and activities such as employment is a fundamental aspect of their wellbeing. Within transport studies, accessibility is considered to be a valuable concept that can be used to generate insights on issues related to social exclusion due to limited access to transport options. Recently, researchers have attempted to link accessibility with popular theories of social justice such as Amartya Sen's Capabilities Approach (CA). Such studies have set the theoretical foundations on the way accessibility can be expressed through the CA, however, attempts to operationalise this approach remain fragmented and predominantly qualitative in nature. The data landscape however, has changed over the last decade providing an unprecedented quantity of transport related data at an individual level. Mobility data from dfferent sources have the potential to contribute to the understanding of individual accessibility and its relation to phenomena such as social exclusion. At the same time, the unlabelled nature of such data present a considerable challenge, as a non-trivial step of inference is required if one is to deduce the transportation modes used and activities reached. This thesis develops a novel framework for accessibility modelling using the CA as theoretical foundation. Within the scope of this thesis, this is used to assess the levels of equality experienced by individuals belonging to different population groups and its link to transport related social exclusion. In the proposed approach, activities reached and transportation modes used are considered manifestations of individual hidden capabilities. A modelling framework using dynamic Bayesian networks is developed to quantify and assess the relationships and dynamics of the different components in fluencing the capabilities sets. The developed approach can also provide inferential capabilities for activity type and transportation mode detection, making it suitable for use with unlabelled mobility data such as Automatic Fare Collection Systems (AFC), mobile phone and social media. The usefulness of the proposed framework is demonstrated through three case studies. In the first case study, mobile phone data were used to explore the interaction of individuals with different public transportation modes. It was found that assumptions about individual mobility preferences derived from travel surveys may not always hold, providing evidence for the significance of personal characteristics to the choices of transportation modes. In the second case, the proposed framework is used for activity type inference, testing the limits of accuracy that can be achieved from unlabelled social media data. A combination of the previous case studies, the third case further defines a generative model which is used to develop the proposed capabilities approach to accessibility model. Using data from London's Automatic Fare Collection Systems (AFC) system, the elements of the capabilities set are explicitly de ned and linked with an individual's personal characteristics, external variables and functionings. The results are used to explore the link between social exclusion and transport disadvantage, revealing distinct patterns that can be attributed to different accessibility levels

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available